6 ms·

No matter angle and illumination? Come on.)

gcr 12y ago
Believe it or not, LFW does not include a whole lot of rotation variance in its images. One reason for this is because LFW was originally selected by an automatic face detector in the first place, which doesn't detect rotated faces very well. Here are some sample images from LFW: http://vis-www.cs.umass.edu/lfw/number_11.html http://vis-www.cs.umass.edu/lfw/number_11.html In almost all of them, the person is usually facing the camera (no profile shots!) and the in-plane rotation is not very large (their eyes are parallel to the horizon; they're not upside-down) The fact that we aren't shooting for rotation-invariance is a problem with LFW, not necessarily this algorithm, but you're right to say that the authors' paper would be much more convincing if they tried their approach on several different datasets. (In particular, the "Multi-PIE" dataset aims to specifically stress rotation-invariance, even though it's not quite as popular as LFW. These guys do use Multi-PIE as part of their training and they do in fact measure their algorithm's performance on Multi-PIE; see page 24. They don't seem to talk about it much since everyone is crazy for LFW and since Multi-PIE is "easy" for other reasons...)
- dschiptsov 12y ago
  So what then is there about "human"? Everyone took the same courses and more or less familiar with the basic algorithms. The task of recognizing a passport-like pictures is nearly trivial, while a "random, on the street" face recognition is a different problem. People are using environmental and contextual cues (so, does facebook's system) and they mostly guessing rather than "exact matching" peoples. Anyway, as long as it is not a passport photos the task of face recognition has almost nothing to do with basic NN algorithms, but a lot with contexts and cues.
  - gcr 12y ago
    Exactly. That's why we should be (IMO) focusing on hard face recognition problems, full of rotation and occlusion and blur and all the "icky" parts of the real world that we don't like to deal with. There is some of that work going on out there, but I would be very surprised to find that spirit in a paper with the words "LFW" in the title. It's just a different focus. In its time, LFW was "hard" since most of the face datasets (FERET, AT&T) were completely controlled: the subject visited a laboratory, sat down in the best pose, made the best face expression, and had their picture taken by the best camera that grant funding could buy. The "in the wild" part of "Labeled Faces in the Wild" alludes to the fact that these are more real-world than what the community was used to. It's time to continue that train of thought and move on to something harder though.

gcr 12y ago

Believe it or not, LFW does not include a whole lot of rotation variance in its images. One reason for this is because LFW was originally selected by an automatic face detector in the first place, which doesn't detect rotated faces very well. Here are some sample images from LFW: http://vis-www.cs.umass.edu/lfw/number_11.html http://vis-www.cs.umass.edu/lfw/number_11.html In almost all of them, the person is usually facing the camera (no profile shots!) and the in-plane rotation is not very large (their eyes are parallel to the horizon; they're not upside-down) The fact that we aren't shooting for rotation-invariance is a problem with LFW, not necessarily this algorithm, but you're right to say that the authors' paper would be much more convincing if they tried their approach on several different datasets. (In particular, the "Multi-PIE" dataset aims to specifically stress rotation-invariance, even though it's not quite as popular as LFW. These guys do use Multi-PIE as part of their training and they do in fact measure their algorithm's performance on Multi-PIE; see page 24. They don't seem to talk about it much since everyone is crazy for LFW and since Multi-PIE is "easy" for other reasons...)

dschiptsov 12y ago
So what then is there about "human"? Everyone took the same courses and more or less familiar with the basic algorithms. The task of recognizing a passport-like pictures is nearly trivial, while a "random, on the street" face recognition is a different problem. People are using environmental and contextual cues (so, does facebook's system) and they mostly guessing rather than "exact matching" peoples. Anyway, as long as it is not a passport photos the task of face recognition has almost nothing to do with basic NN algorithms, but a lot with contexts and cues.
- gcr 12y ago
  Exactly. That's why we should be (IMO) focusing on hard face recognition problems, full of rotation and occlusion and blur and all the "icky" parts of the real world that we don't like to deal with. There is some of that work going on out there, but I would be very surprised to find that spirit in a paper with the words "LFW" in the title. It's just a different focus. In its time, LFW was "hard" since most of the face datasets (FERET, AT&T) were completely controlled: the subject visited a laboratory, sat down in the best pose, made the best face expression, and had their picture taken by the best camera that grant funding could buy. The "in the wild" part of "Labeled Faces in the Wild" alludes to the fact that these are more real-world than what the community was used to. It's time to continue that train of thought and move on to something harder though.